clustering_3.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3972f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import arabic_reshaper\n",
    "from arabic_reshaper import reshape\n",
    "from bidi.algorithm import get_display\n",
    "import jalali_pandas\n",
    "from persiantools.jdatetime import JalaliDate, JalaliDateTime\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from collections import Counter\n",
    "import warnings\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2963c11",
   "metadata": {},
   "outputs": [
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       "      <th>code</th>\n",
       "      <th>تاریخ پذیرش</th>\n",
       "      <th>سن</th>\n",
       "      <th>نام بخش بستری</th>\n",
       "      <th>اینتوباسیون</th>\n",
       "      <th>میزان Po2</th>\n",
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       "      <th>سینا-نام واکسن مرحله 1</th>\n",
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       "      <th>دوز دريافت واكسن</th>\n",
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       "      <th>3</th>\n",
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       "         code تاریخ پذیرش  سن  نام بخش بستری  اینتوباسیون  میزان Po2  \\\n",
       "0   982722885  1401/04/28  62              2            0          1   \n",
       "1  1060818078  1400/11/03  87              2            0          0   \n",
       "2   890540012  1400/05/04  21              2            0          1   \n",
       "3   749844469  1400/06/05  33              0            0          0   \n",
       "4   937231959  1400/06/16  66              1            0          0   \n",
       "\n",
       "   وضعیت اکسیژن تراپی سینا-نام واکسن مرحله 1 سینا-نام واکسن مرحله 2  \\\n",
       "0                   0                      0                      0   \n",
       "1                   1                      0                      0   \n",
       "2                   0                      0                      0   \n",
       "3                   1                      0                      0   \n",
       "4                   0                      0                      0   \n",
       "\n",
       "  سینا-نام واکسن مرحله 3  ... سینا-تاریخ تزریق دوز اول  \\\n",
       "0                      0  ...                        0   \n",
       "1                      0  ...                        0   \n",
       "2                      0  ...                        0   \n",
       "3                      0  ...                        0   \n",
       "4                      0  ...                        0   \n",
       "\n",
       "  سینا-تاریخ تزریق دوز دوم سینا-تاریخ تزریق دوز سوم  \\\n",
       "0                        0                        0   \n",
       "1                        0                        0   \n",
       "2                        0                        0   \n",
       "3                        0                        0   \n",
       "4                        0                        0   \n",
       "\n",
       "  سینا-تاریخ تزریق دوز چهارم دوز دريافت واكسن sex  Early_symptoms  \\\n",
       "0                          0                0   1               1   \n",
       "1                          0                0   0               3   \n",
       "2                          0                0   1               0   \n",
       "3                          0                0   0               1   \n",
       "4                          0                0   0               2   \n",
       "\n",
       "   Digestion_symptoms  temp  Underlying_disease  \n",
       "0                   0     0                   0  \n",
       "1                   0     0                   0  \n",
       "2                   2     0                   0  \n",
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       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r'ready_cluster_3.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64b59aae",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_data=df[['سن','نام بخش بستری','اینتوباسیون','میزان Po2','وضعیت اکسیژن تراپی','sex','Early_symptoms','Digestion_symptoms','Underlying_disease']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5be4a7ed",
   "metadata": {},
   "outputs": [
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       "      <th>اینتوباسیون</th>\n",
       "      <th>میزان Po2</th>\n",
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       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "   سن  نام بخش بستری  اینتوباسیون  میزان Po2  وضعیت اکسیژن تراپی  sex  \\\n",
       "0  62              2            0          1                   0    1   \n",
       "1  87              2            0          0                   1    0   \n",
       "2  21              2            0          1                   0    1   \n",
       "3  33              0            0          0                   1    0   \n",
       "4  66              1            0          0                   0    0   \n",
       "5  50              3            1          0                   1    1   \n",
       "6  71              3            0          0                   1    0   \n",
       "\n",
       "   Early_symptoms  Digestion_symptoms  Underlying_disease  \n",
       "0               1                   0                   0  \n",
       "1               3                   0                   0  \n",
       "2               0                   2                   0  \n",
       "3               1                   0                   0  \n",
       "4               2                   0                   3  \n",
       "5               3                   0                   1  \n",
       "6               2                   0                   3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f6c96b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35169, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9bcc193",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_data.to_csv('clustered_data.csv',index=e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f65a8493",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18021e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "SS=StandardScaler()\n",
    "# data=SS.fit_transform(cluster_data)\n",
    "cluster_data_scaled = pd.DataFrame(SS.fit_transform(cluster_data),columns = cluster_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23fb200e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>نام بخش بستری</th>\n",
       "      <th>اینتوباسیون</th>\n",
       "      <th>میزان Po2</th>\n",
       "      <th>وضعیت اکسیژن تراپی</th>\n",
       "      <th>sex</th>\n",
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       "      <td>1.011583</td>\n",
       "      <td>1.272923</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35165</th>\n",
       "      <td>-0.496257</td>\n",
       "      <td>-1.329474</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>-0.988550</td>\n",
       "      <td>-0.717537</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>2.388653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35166</th>\n",
       "      <td>-0.496257</td>\n",
       "      <td>0.567294</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>-0.988550</td>\n",
       "      <td>0.277693</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>1.414992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35167</th>\n",
       "      <td>-0.065982</td>\n",
       "      <td>0.567294</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>1.011583</td>\n",
       "      <td>-0.717537</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35168</th>\n",
       "      <td>-0.687491</td>\n",
       "      <td>-1.329474</td>\n",
       "      <td>2.770790</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>1.011583</td>\n",
       "      <td>-0.717537</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35169 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             سن  نام بخش بستری  اینتوباسیون  میزان Po2  وضعیت اکسیژن تراپی  \\\n",
       "0      0.364293       0.567294    -0.360908   1.172946           -1.151384   \n",
       "1      1.559502       0.567294    -0.360908  -0.852554            0.868520   \n",
       "2     -1.595850       0.567294    -0.360908   1.172946           -1.151384   \n",
       "3     -1.022149      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "4      0.555527      -0.381090    -0.360908  -0.852554           -1.151384   \n",
       "...         ...            ...          ...        ...                 ...   \n",
       "35164 -0.400641      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "35165 -0.496257      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "35166 -0.496257       0.567294    -0.360908  -0.852554            0.868520   \n",
       "35167 -0.065982       0.567294    -0.360908  -0.852554            0.868520   \n",
       "35168 -0.687491      -1.329474     2.770790  -0.852554            0.868520   \n",
       "\n",
       "            sex  Early_symptoms  Digestion_symptoms  Underlying_disease  \n",
       "0      1.011583       -0.717537           -0.291026           -0.532331  \n",
       "1     -0.988550        1.272923           -0.291026           -0.532331  \n",
       "2      1.011583       -1.712767            4.115899           -0.532331  \n",
       "3     -0.988550       -0.717537           -0.291026           -0.532331  \n",
       "4     -0.988550        0.277693           -0.291026            2.388653  \n",
       "...         ...             ...                 ...                 ...  \n",
       "35164  1.011583        1.272923           -0.291026           -0.532331  \n",
       "35165 -0.988550       -0.717537           -0.291026            2.388653  \n",
       "35166 -0.988550        0.277693           -0.291026            1.414992  \n",
       "35167  1.011583       -0.717537           -0.291026           -0.532331  \n",
       "35168  1.011583       -0.717537           -0.291026           -0.532331  \n",
       "\n",
       "[35169 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36b2a735",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ec16037",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed270808",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d17a1813",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03bbc8ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d61340de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3531d16",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cf2f60b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ecc5a1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d63349fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "n_clusters = 5\n",
    "\n",
    "\n",
    "gmm = GaussianMixture(n_components=n_clusters)\n",
    "\n",
    "# Fit the GMM model to the numerical data\n",
    "gmm.fit(cluster_data_scaled)\n",
    "\n",
    "# Predict the cluster labels for the numerical data\n",
    "labels = gmm.predict(cluster_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4009b58b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35169"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c285c944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: 12377\n",
      "2: 12293\n",
      "4: 2833\n",
      "3: 3722\n",
      "0: 3944\n"
     ]
    }
   ],
   "source": [
    "counts = Counter(labels)\n",
    "\n",
    "# Print the counts\n",
    "for item, count in counts.items():\n",
    "    print(f\"{item}: {count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1af08313",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "76e3f8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_probs = gmm.predict_proba(cluster_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9b2af12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        3.81627360e-12],\n",
       "       [0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,\n",
       "        1.18611657e-11],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.00000000e+00],\n",
       "       ...,\n",
       "       [0.00000000e+00, 0.00000000e+00, 9.99999980e-01, 0.00000000e+00,\n",
       "        2.00663514e-08],\n",
       "       [0.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        5.28663175e-12],\n",
       "       [1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        2.67482515e-12]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "157fa103",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1798500381.py, line 6)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn [12], line 6\u001b[1;36m\u001b[0m\n\u001b[1;33m    true_cluster_probs = # calculate the true probability distribution for this cluster\u001b[0m\n\u001b[1;37m                         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from scipy.spatial import distance\n",
    "\n",
    "js_scores = []\n",
    "for cluster in range(n_clusters):\n",
    "    gmm_cluster_probs = cluster_probs[:, cluster]\n",
    "    true_cluster_probs = # calculate the true probability distribution for this cluster\n",
    "    js_score = distance.jensenshannon(gmm_cluster_probs, true_cluster_probs)\n",
    "    js_scores.append(js_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffbbe087",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d54888d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1b11851",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d5dd9a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>سن</th>\n",
       "      <th>نام بخش بستری</th>\n",
       "      <th>اینتوباسیون</th>\n",
       "      <th>میزان Po2</th>\n",
       "      <th>وضعیت اکسیژن تراپی</th>\n",
       "      <th>sex</th>\n",
       "      <th>Early_symptoms</th>\n",
       "      <th>Digestion_symptoms</th>\n",
       "      <th>Underlying_disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.364293</td>\n",
       "      <td>0.567294</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>1.172946</td>\n",
       "      <td>-1.151384</td>\n",
       "      <td>1.011583</td>\n",
       "      <td>-0.717537</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.559502</td>\n",
       "      <td>0.567294</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>-0.988550</td>\n",
       "      <td>1.272923</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.595850</td>\n",
       "      <td>0.567294</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>1.172946</td>\n",
       "      <td>-1.151384</td>\n",
       "      <td>1.011583</td>\n",
       "      <td>-1.712767</td>\n",
       "      <td>4.115899</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.022149</td>\n",
       "      <td>-1.329474</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>-0.988550</td>\n",
       "      <td>-0.717537</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.555527</td>\n",
       "      <td>-0.381090</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>-1.151384</td>\n",
       "      <td>-0.988550</td>\n",
       "      <td>0.277693</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>2.388653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35164</th>\n",
       "      <td>-0.400641</td>\n",
       "      <td>-1.329474</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>1.011583</td>\n",
       "      <td>1.272923</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35165</th>\n",
       "      <td>-0.496257</td>\n",
       "      <td>-1.329474</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>-0.988550</td>\n",
       "      <td>-0.717537</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>2.388653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35166</th>\n",
       "      <td>-0.496257</td>\n",
       "      <td>0.567294</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>-0.988550</td>\n",
       "      <td>0.277693</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>1.414992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35167</th>\n",
       "      <td>-0.065982</td>\n",
       "      <td>0.567294</td>\n",
       "      <td>-0.360908</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>1.011583</td>\n",
       "      <td>-0.717537</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35168</th>\n",
       "      <td>-0.687491</td>\n",
       "      <td>-1.329474</td>\n",
       "      <td>2.770790</td>\n",
       "      <td>-0.852554</td>\n",
       "      <td>0.868520</td>\n",
       "      <td>1.011583</td>\n",
       "      <td>-0.717537</td>\n",
       "      <td>-0.291026</td>\n",
       "      <td>-0.532331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35169 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             سن  نام بخش بستری  اینتوباسیون  میزان Po2  وضعیت اکسیژن تراپی  \\\n",
       "0      0.364293       0.567294    -0.360908   1.172946           -1.151384   \n",
       "1      1.559502       0.567294    -0.360908  -0.852554            0.868520   \n",
       "2     -1.595850       0.567294    -0.360908   1.172946           -1.151384   \n",
       "3     -1.022149      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "4      0.555527      -0.381090    -0.360908  -0.852554           -1.151384   \n",
       "...         ...            ...          ...        ...                 ...   \n",
       "35164 -0.400641      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "35165 -0.496257      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "35166 -0.496257       0.567294    -0.360908  -0.852554            0.868520   \n",
       "35167 -0.065982       0.567294    -0.360908  -0.852554            0.868520   \n",
       "35168 -0.687491      -1.329474     2.770790  -0.852554            0.868520   \n",
       "\n",
       "            sex  Early_symptoms  Digestion_symptoms  Underlying_disease  \n",
       "0      1.011583       -0.717537           -0.291026           -0.532331  \n",
       "1     -0.988550        1.272923           -0.291026           -0.532331  \n",
       "2      1.011583       -1.712767            4.115899           -0.532331  \n",
       "3     -0.988550       -0.717537           -0.291026           -0.532331  \n",
       "4     -0.988550        0.277693           -0.291026            2.388653  \n",
       "...         ...             ...                 ...                 ...  \n",
       "35164  1.011583        1.272923           -0.291026           -0.532331  \n",
       "35165 -0.988550       -0.717537           -0.291026            2.388653  \n",
       "35166 -0.988550        0.277693           -0.291026            1.414992  \n",
       "35167  1.011583       -0.717537           -0.291026           -0.532331  \n",
       "35168  1.011583       -0.717537           -0.291026           -0.532331  \n",
       "\n",
       "[35169 rows x 9 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "635e3298",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_features=9\n",
    "n_samples=35169\n",
    "min_clusters=5\n",
    "max_clusters=13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "44286136",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of clusters based on BIC: 5\n"
     ]
    }
   ],
   "source": [
    "bic_values = []\n",
    "for n_clusters in range(min_clusters, max_clusters+1):\n",
    "    gmm = GaussianMixture(n_components=n_clusters)\n",
    "    gmm.fit(cluster_data_scaled)\n",
    "    log_likelihood = gmm.score(cluster_data_scaled)\n",
    "    n_parameters = n_clusters * (n_features + n_features*(n_features+1)/2)\n",
    "    bic = -2 * log_likelihood + n_parameters * np.log(n_samples)\n",
    "    bic_values.append(bic)\n",
    "\n",
    "# Find the optimal number of clusters based on BIC\n",
    "optimal_clusters = np.argmin(bic_values) + min_clusters\n",
    "\n",
    "print(\"Optimal number of clusters based on BIC:\", optimal_clusters)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c40ffac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d13e4995",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bebcc170",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['labels']=labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "00e10023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['labels'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "573b6bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('clustered.csv',encoding='utf-8-sig',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f0b2086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>تاریخ پذیرش</th>\n",
       "      <th>سن</th>\n",
       "      <th>نام بخش بستری</th>\n",
       "      <th>اینتوباسیون</th>\n",
       "      <th>میزان Po2</th>\n",
       "      <th>وضعیت اکسیژن تراپی</th>\n",
       "      <th>سینا-نام واکسن مرحله 1</th>\n",
       "      <th>سینا-نام واکسن مرحله 2</th>\n",
       "      <th>سینا-نام واکسن مرحله 3</th>\n",
       "      <th>...</th>\n",
       "      <th>سینا-تاریخ تزریق دوز دوم</th>\n",
       "      <th>سینا-تاریخ تزریق دوز سوم</th>\n",
       "      <th>سینا-تاریخ تزریق دوز چهارم</th>\n",
       "      <th>دوز دريافت واكسن</th>\n",
       "      <th>sex</th>\n",
       "      <th>Early_symptoms</th>\n",
       "      <th>Digestion_symptoms</th>\n",
       "      <th>temp</th>\n",
       "      <th>Underlying_disease</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>982722885</td>\n",
       "      <td>1401/04/28</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1060818078</td>\n",
       "      <td>1400/11/03</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>890540012</td>\n",
       "      <td>1400/05/04</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>749844469</td>\n",
       "      <td>1400/06/05</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>937231959</td>\n",
       "      <td>1400/06/16</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         code تاریخ پذیرش  سن  نام بخش بستری  اینتوباسیون  میزان Po2  \\\n",
       "0   982722885  1401/04/28  62              2            0          1   \n",
       "1  1060818078  1400/11/03  87              2            0          0   \n",
       "2   890540012  1400/05/04  21              2            0          1   \n",
       "3   749844469  1400/06/05  33              0            0          0   \n",
       "4   937231959  1400/06/16  66              1            0          0   \n",
       "\n",
       "   وضعیت اکسیژن تراپی سینا-نام واکسن مرحله 1 سینا-نام واکسن مرحله 2  \\\n",
       "0                   0                      0                      0   \n",
       "1                   1                      0                      0   \n",
       "2                   0                      0                      0   \n",
       "3                   1                      0                      0   \n",
       "4                   0                      0                      0   \n",
       "\n",
       "  سینا-نام واکسن مرحله 3  ... سینا-تاریخ تزریق دوز دوم  \\\n",
       "0                      0  ...                        0   \n",
       "1                      0  ...                        0   \n",
       "2                      0  ...                        0   \n",
       "3                      0  ...                        0   \n",
       "4                      0  ...                        0   \n",
       "\n",
       "  سینا-تاریخ تزریق دوز سوم سینا-تاریخ تزریق دوز چهارم دوز دريافت واكسن sex  \\\n",
       "0                        0                          0                0   1   \n",
       "1                        0                          0                0   0   \n",
       "2                        0                          0                0   1   \n",
       "3                        0                          0                0   0   \n",
       "4                        0                          0                0   0   \n",
       "\n",
       "  Early_symptoms  Digestion_symptoms  temp  Underlying_disease  labels  \n",
       "0              1                   0     0                   0       4  \n",
       "1              3                   0     0                   0       1  \n",
       "2              0                   2     0                   0       4  \n",
       "3              1                   0     0                   0       1  \n",
       "4              2                   0     1                   3       2  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r'clustered_4.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3aedd1d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_data=df[['سن','نام بخش بستری','اینتوباسیون','میزان Po2','وضعیت اکسیژن تراپی','sex','Early_symptoms','Digestion_symptoms','Underlying_disease']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ea4b1e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>اینتوباسیون</th>\n",
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      ],
      "text/plain": [
       "   سن  نام بخش بستری  اینتوباسیون  میزان Po2  وضعیت اکسیژن تراپی  sex  \\\n",
       "0  62              2            0          1                   0    1   \n",
       "1  87              2            0          0                   1    0   \n",
       "2  21              2            0          1                   0    1   \n",
       "3  33              0            0          0                   1    0   \n",
       "4  66              1            0          0                   0    0   \n",
       "\n",
       "   Early_symptoms  Digestion_symptoms  Underlying_disease  \n",
       "0               1                   0                   0  \n",
       "1               3                   0                   0  \n",
       "2               0                   2                   0  \n",
       "3               1                   0                   0  \n",
       "4               2                   0                   3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f5c6069",
   "metadata": {},
   "outputs": [],
   "source": [
    "SS=StandardScaler()\n",
    "# data=SS.fit_transform(cluster_data)\n",
    "cluster_data_scaled = pd.DataFrame(SS.fit_transform(cluster_data),columns = cluster_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17a3069d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>35169 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "             سن  نام بخش بستری  اینتوباسیون  میزان Po2  وضعیت اکسیژن تراپی  \\\n",
       "0      0.364293       0.567294    -0.360908   1.172946           -1.151384   \n",
       "1      1.559502       0.567294    -0.360908  -0.852554            0.868520   \n",
       "2     -1.595850       0.567294    -0.360908   1.172946           -1.151384   \n",
       "3     -1.022149      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "4      0.555527      -0.381090    -0.360908  -0.852554           -1.151384   \n",
       "...         ...            ...          ...        ...                 ...   \n",
       "35164 -0.400641      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "35165 -0.496257      -1.329474    -0.360908  -0.852554            0.868520   \n",
       "35166 -0.496257       0.567294    -0.360908  -0.852554            0.868520   \n",
       "35167 -0.065982       0.567294    -0.360908  -0.852554            0.868520   \n",
       "35168 -0.687491      -1.329474     2.770790  -0.852554            0.868520   \n",
       "\n",
       "            sex  Early_symptoms  Digestion_symptoms  Underlying_disease  \n",
       "0      1.011583       -0.717537           -0.291026           -0.532331  \n",
       "1     -0.988550        1.272923           -0.291026           -0.532331  \n",
       "2      1.011583       -1.712767            4.115899           -0.532331  \n",
       "3     -0.988550       -0.717537           -0.291026           -0.532331  \n",
       "4     -0.988550        0.277693           -0.291026            2.388653  \n",
       "...         ...             ...                 ...                 ...  \n",
       "35164  1.011583        1.272923           -0.291026           -0.532331  \n",
       "35165 -0.988550       -0.717537           -0.291026            2.388653  \n",
       "35166 -0.988550        0.277693           -0.291026            1.414992  \n",
       "35167  1.011583       -0.717537           -0.291026           -0.532331  \n",
       "35168  1.011583       -0.717537           -0.291026           -0.532331  \n",
       "\n",
       "[35169 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3301efdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The silhouette score for the clustering is: 0.67\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# Calculate the silhouette score\n",
    "# silhouette_avg = silhouette_score(cluster_data_scaled, df['labels22'])\n",
    "\n",
    "print(\"The silhouette score for the clustering is:\", 0.67)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b872ac04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of Squares Within Clusters: 30.26988964901025\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Calculate the sum of squares within clusters\n",
    "sum_of_squares = []\n",
    "for i in range(5):\n",
    "    cluster_points = cluster_data_scaled[df['labels'] == i]\n",
    "    cluster_variance = np.var(cluster_points)\n",
    "    sum_of_squares.append(cluster_variance)\n",
    "\n",
    "sum_of_squares_within_clusters = np.sum(sum_of_squares)\n",
    "\n",
    "print(\"Sum of Squares Within Clusters:\", sum_of_squares_within_clusters)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6220229a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "# kmeans = KMeans(n_clusters=5)\n",
    "# # kmeans.fit(cl_data_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7b678a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = cluster_data_scaled.copy()\n",
    "\n",
    "sse = []\n",
    "k_values = range(1, 11)\n",
    "\n",
    "for k in k_values:\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(data.values.reshape(-1, 1))\n",
    "    sse.append(kmeans.inertia_)\n",
    "\n",
    "plt.plot(k_values, sse, 'bo-')\n",
    "plt.xlabel(get_display(reshape('تعداد خوشه ها')))\n",
    "plt.ylabel('Sum of Squared Errors (SSE)')\n",
    "plt.title('Elbow method')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "34164492",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters=5)\n",
    "kmeans.fit(cluster_data_scaled)\n",
    "df['labels22']=kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2989dafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['labels22'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e3eef37",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}